Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA.
Lawrence Livermore National Laboratory, Livermore, CA 95616, USA.
Sensors (Basel). 2022 Aug 2;22(15):5782. doi: 10.3390/s22155782.
Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.
利用雷达进行准确的人员身份识别具有多种潜在应用，如监控、访问控制和安全检查站。然而，基于雷达的人员识别一直局限于少数仅依赖于微多普勒特征的基于运动的生物识别技术。本文首次提出将基于雷达的心跳和步态信号结合起来作为人员识别的生物特征。所提出的方法首先将从 18 个对象中提取的生物特征签名转换为图像，然后应用图像增强技术，并使用深度迁移学习对每个对象进行分类。报告了心跳和步态生物特征的验证精度分别为 58.7%和 96%。接下来，使用联合概率质量函数 (PMF) 方法结合两种生物特征的识别结果，报告了 98%的识别准确率。据我们所知，这是迄今为止文献中报道的最高准确率。最后，在实际场景中测试训练好的网络，并在办公访问控制平台中用于识别不同的人类对象。我们报告的准确率为 76.25%。
